Abstract
The paper proposes an angular velocity fusion method of the microelectromechanical system inertial measurement unit array based on the extended Kalman filter with correlated system noises. In the proposed method, an adaptive model of the angular velocity is built according to the motion characteristics of the vehicles and it is regarded as the state equation to estimate the angular velocity. The signal model of gyroscopes and accelerometers in the microelectromechanical system inertial measurement unit array is used as the measurement equation to fuse and estimate the angular velocity. Due to the correlation of the state and measurement noises in the presented fusion model, the traditional extended Kalman filter equations are optimized, so as to accurately and reliably estimate the angular velocity. By simulating angular rates in different motion modes, such as constant and change-in-time angular rates, it is verified that the proposed method can reliably estimate angular rates, even when the angular rate has been out of the microelectromechanical system gyroscope measurement range. And results show that, compared with the traditional angular rate fusion method of microelectromechanical system inertial measurement unit array, it can estimate angular rates more accurately. Moreover, in the kinematic vehicle experiments, the performance advantage of the proposed method is also verified and the angular rate estimation accuracy can be increased by about 1.5 times compared to the traditional method.
Highlights
The angular velocity of the vehicles, such as the multirotor unmanned aerial vehicle (UAV), the car or other vehicles,1–5 is usually measured by microelectromechanical system (MEMS) gyroscopes equipped in the navigation system
On the basis of the above analysis and being inspired by the fusion method of the MIMU array in Skog et al.,24 this paper proposes an improved angular velocity fusion method of the MIMU array
Even when the Z-axis angular rate is out of the gyroscope measurement range seen from Figures 4 and 8, applying the two methods can estimate the angular rate accurately, which is consistent with the conclusion of section ‘‘maximum likelihood estimation (MLE)-based fusion method.’’ To analyze and compare the performance differences between MLE and our proposed optimization extended Kalman filter (EKF), the triaxial angular rate estimation errors under the conditions of constant and change-in-time angular rates are calculated, respectively, as shown in Figures 9 and 10, that is, corresponding to Figures 6–8
Summary
The angular velocity of the vehicles, such as the multirotor unmanned aerial vehicle (UAV), the car or other vehicles,1–5 is usually measured by microelectromechanical system (MEMS) gyroscopes equipped in the navigation system. In the MIMU array, because the accelerometer signal model has a non-linear relationship with the angular velocity (seen in equation [2]), an EKF-based fusion method to estimate the angular velocity is proposed .
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